Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This paper addresses PFL via explicit disentangling latent representations into two parts to capture the shared knowledge and client-specific personalization, which leads to more reliable and effective PFL. The disentanglement is achieved by a novel Federated Dual Variational Autoencoder (FedDVA), which employs two encoders to infer the two types of representations. FedDVA can produce a better understanding of the trade-off between global knowledge sharing and local personalization in PFL. Moreover, it can be integrated with existing FL methods and turn them into personalized models for heterogeneous downstream tasks. Extensive experiments validate the advantages caused by disentanglement and show that models trained with disentangled representations substantially outperform those vanilla methods.

翻译：个性化联邦学习（PFL）通过在客户端间的知识共享与每个客户端的模型个性化之间取得平衡，联合训练多种局部模型。本文通过将隐层表示显式解耦为两部分，分别捕获共享知识与客户端特定的个性化信息，从而实现了更可靠和有效的PFL。该解耦过程由一种新颖的联邦双变分自编码器（FedDVA）实现，该模型采用两个编码器来推断这两种类型的表示。FedDVA能够更深入地理解PFL中全局知识共享与局部个性化之间的权衡。此外，它可集成现有FL方法，并将其转化为适用于异构下游任务的个性化模型。大量实验验证了解耦带来的优势，并表明使用解耦表示训练的模型显著优于传统方法。